Submersion detection, underwater depth and low-latency temperature estimation using wearable device

Abstract

Embodiments are disclosed for submersion detection and underwater depth and low-latency temperature estimation. In an embodiment, a method comprises: determining a first set of vertical accelerations obtained from an inertial sensor of a wearable device; determining a second set of vertical accelerations obtained from pressure data; determining a first feature associated with a correlation between the first and second sets of vertical accelerations; and determining that the wearable device is submerged or not submerged in water based on a machine learning model applied to the first feature. In another embodiment, a method comprises: determining a submersion state of a wearable device; and responsive to the submersion state being submerged, computing a forward estimate of water temperature based on measured ambient water temperature at the water surface, a temperature error lookup table, and a rate of change of the ambient water temperature.

What is claimed is: 
     
         1 . A method comprising:
 determining, with at least one processor, a first set of vertical accelerations obtained from an inertial sensor of a wearable device;   determining, with the at least one processor, a second set of vertical accelerations obtained from pressure data;   determining, with the at least one processor, a first feature associated with a correlation between the first and second sets of vertical accelerations; and   determining, with the at least one processor, whether the wearable device is submerged or not submerged in water based on a machine learning model applied to the first feature.   
     
     
         2 . The method of  claim 2 , further comprising:
 determining, with the at least one processor, a second feature associated with a slope of a line fitted to a plot of the first set of vertical accelerations and the second set of vertical accelerations; and   determining, with the at least one processor, that the wearable device is submerged or not submerged in the water based on the machine learning model applied to the first feature and the second feature.   
     
     
         3 . The method of  claim 2 , further comprising:
 determining, with the at least one processor, a second feature associated with a touch screen gesture; and   determining, with the at least one processor, whether the wearable device is submerged or not submerged in the water based the machine learning model applied to the first feature and the second feature.   
     
     
         4 . The method of  claim 1 , wherein responsive to determining that the wearable device is submerged, the method further comprises:
 estimating, with the at least one processor, a depth of the wearable device in the water based on a measured pressure and a measured ambient air pressure at the water surface computed and stored by the wearable device prior to the wearable device being submerged in the water.   
     
     
         5 . The method of  claim 4 , wherein determining whether the wearable device is submerged or not submerged in the water further comprises:
 comparing the estimated depth with a minimum depth threshold; and   if the estimated depth exceeds a minimum depth threshold, determining whether the wearable device is submerged or not submerged in water.   
     
     
         6 . The method of  claim 4 , wherein the ambient air pressure at the surface is measured each time the first set of vertical accelerations are above a minimum threshold and a range of measured pressure change is less than or equal to a specified pressure threshold. 
     
     
         7 . The method of  claim 4 , where the ambient air pressure is filtered to remove potential outliers due to a prior exposure of a pressure sensor of the wearable device to water. 
     
     
         8 . A method comprising:
 determining, with at least one processor, a water submersion state of a wearable device; and   responsive to the water submersion state being submerged, computing, with the at least one processor, a forward estimate of the water temperature based on a measured water temperature, a temperature error lookup table, and a rate of change of the ambient water temperature.   
     
     
         9 . The method of  claim 7 , wherein the forward estimate of the water temperature is estimated by a first order forward temperature predictor given by:
     {circumflex over (T)}   water   =T ( t )−Δ T ( {dot over (T)} ( t )),
   
       where {circumflex over (T)} water  is the forward estimate of water temperature, T (t) is the measured ambient water temperature at time t, ΔT is a temperature error from a pre-established lookup table, as a function of {dot over (T)}(t), and {dot over (T)}(t) is the rate of change of temperature at time t. 
     
     
         10 . An apparatus comprising:
 at least one motion sensor;   at least one pressure sensor;   at least one processor;   memory storing instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising:
 determining a first set of vertical accelerations obtained from the motion sensor; 
 determining a second set of vertical accelerations obtained from pressure data measured by the at least one pressure sensor; 
 determining a first feature associated with a correlation between the first and second sets of vertical accelerations; and 
 determining whether the apparatus is submerged or not submerged in water based on a machine learning model applied to the first feature. 
   
     
     
         11 . The apparatus of  claim 10 , further comprising:
 determining a second feature associated with a slope of a line fitted to a plot of the first set of vertical accelerations and the second set of vertical accelerations; and   determining that the apparatus is submerged or not submerged in the water based on the machine learning model applied to the first feature and the second feature.   
     
     
         12 . The apparatus of  claim 11 , wherein the apparatus includes a touch screen, and the operations further comprise:
 determining a second feature associated with a touch screen gesture; and   determining whether the apparatus is submerged or not submerged in the water based the machine learning model applied to the first feature and the second feature.   
     
     
         13 . The apparatus of  claim 10 , wherein responsive to determining that the apparatus is submerged, the method further comprises:
 estimating a depth of the apparatus in the water based on a measured pressure and a measured ambient air pressure at the water surface computed and stored by the apparatus prior to the apparatus being submerged in the water.   
     
     
         14 . The apparatus of  claim 13 , wherein determining whether the apparatus is submerged or not submerged in the water further comprises:
 comparing the estimated depth with a minimum depth threshold; and   if the estimated depth exceeds a minimum depth threshold, determining whether the apparatus is submerged or not submerged in water.   
     
     
         15 . The apparatus of  claim 13 , wherein the ambient air pressure at the surface is measured each time the first set of vertical accelerations are above a minimum threshold and a range of measured pressure change is less than or equal to a specified pressure threshold. 
     
     
         16 . The apparatus of  claim 13 , where the ambient air pressure is filtered to remove potential outliers due to a prior exposure of a pressure sensor of the wearable device to water. 
     
     
         17 . An apparatus comprising:
 at least one temperature sensor;   at least one processor;   memory storing instructions that when executed by the at least one processor, cause the at least one processor to perform operations comprising:
 determining a water submersion state of a wearable device; and 
 responsive to the water submersion state being submerged, computing a forward estimate of the water temperature based on ambient water temperature measured by the at least one temperature sensor, a temperature error lookup table, and a rate of change of the ambient water temperature. 
   
     
     
         18 . The apparatus of  claim 17 , wherein the forward estimate of the water temperature is estimated by a first order forward temperature predictor given by:
     {circumflex over (T)}   water   =T ( t )−Δ T ( {dot over (T)} ( t )),
   
       where {circumflex over (T)} water  is the forward estimate of water temperature, T(t) is the ambient air temperature measured by the at least one temperature sensor at the water surface at time t, ΔT is a temperature error from a pre-established lookup table as a function of {dot over (T)}(t), and {dot over (T)}(t) is the rate of change of temperature at time t.